diff --git a/tensorflow_probability/python/experimental/mcmc/weighted_resampling.py b/tensorflow_probability/python/experimental/mcmc/weighted_resampling.py
index 5dfff6807a..aa0bfe92e0 100644
--- a/tensorflow_probability/python/experimental/mcmc/weighted_resampling.py
+++ b/tensorflow_probability/python/experimental/mcmc/weighted_resampling.py
@@ -79,8 +79,12 @@ def resample(particles, log_weights, resample_fn, target_log_weights=None,
 
     # Normalize the weights and sample the ancestral indices.
     log_probs = tf.math.log_softmax(log_weights, axis=particles_dim)
-    resampled_indices = resample_fn(log_probs, num_particles, (),
-                                    particles_dim=particles_dim, seed=seed)
+    if particles_dim == 0:
+        # For resample functions that don't yet support the particles_dim argument.
+        resampled_indices = resample_fn(log_probs, num_particles, (), seed=seed)
+    else:
+        resampled_indices = resample_fn(log_probs, num_particles, (),
+                                        particles_dim=particles_dim, seed=seed)
 
     gather_ancestors = lambda x: (  # pylint: disable=g-long-lambda
         mcmc_util.index_remapping_gather(x,
@@ -281,6 +285,9 @@ def resample_systematic(log_probs, event_size, sample_shape,
       The remaining dimensions are batch dimensions.
     event_size: the dimension of the vector considered a single draw.
     sample_shape: the `sample_shape` determining the number of draws.
+    particles_dim: Python `int` axis of each state `Tensor` indexing into the
+      particles. This is almost always zero, but nonzero values may be necessary
+      when running SMC in nested contexts.
     seed: PRNG seed; see `tfp.random.sanitize_seed` for details.
       Default value: None (i.e. no seed).
     name: Python `str` name for ops created by this method.